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Simulation of financial market via nonlinear Ising model

Author

Listed:
  • Bonggyun Ko

    (Department of Industrial Engineering, Seoul National University, Seoul 151-742, Republic of Korea)

  • Jae Wook Song

    (Department of Industrial Engineering, Seoul National University, Seoul 151-742, Republic of Korea)

  • Woojin Chang

    (Department of Industrial Engineering, Seoul National University, Seoul 151-742, Republic of Korea)

Abstract

In this research, we propose a practical method for simulating the financial return series whose distribution has a specific heaviness. We employ the Ising model for generating financial return series to be analogous to those of the real series. The similarity between real financial return series and simulated one is statistically verified based on their stylized facts including the power law behavior of tail distribution. We also suggest the scheme for setting the parameters in order to simulate the financial return series with specific tail behavior. The simulation method introduced in this paper is expected to be applied to the other financial products whose price return distribution is fat-tailed.

Suggested Citation

  • Bonggyun Ko & Jae Wook Song & Woojin Chang, 2016. "Simulation of financial market via nonlinear Ising model," International Journal of Modern Physics C (IJMPC), World Scientific Publishing Co. Pte. Ltd., vol. 27(04), pages 1-15, April.
    • Handle: RePEc:wsi:ijmpcx:v:27:y:2016:i:04:n:s0129183116500388
    DOI: 10.1142/S0129183116500388
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    References listed on IDEAS

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    1. Slanina, Frantisek, 2013. "Essentials of Econophysics Modelling," OUP Catalogue, Oxford University Press, number 9780199299683.
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    Citations

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    Cited by:

    1. Quanbo Zha & Gang Kou & Hengjie Zhang & Haiming Liang & Xia Chen & Cong-Cong Li & Yucheng Dong, 2020. "Opinion dynamics in finance and business: a literature review and research opportunities," Financial Innovation, Springer;Southwestern University of Finance and Economics, vol. 6(1), pages 1-22, December.
    2. Jia, Linlu & Ke, Jinchuan & Wang, Jun, 2019. "Volatility aggregation intensity energy futures series on stochastic finite-range exclusion dynamics," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 514(C), pages 370-383.
    3. Zhang, Wei & Wang, Jun, 2017. "Nonlinear stochastic exclusion financial dynamics modeling and time-dependent intrinsic detrended cross-correlation," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 482(C), pages 29-41.
    4. Yue Chen & Xiaojian Niu & Yan Zhang, 2019. "Exploring Contrarian Degree in the Trading Behavior of China's Stock Market," Complexity, Hindawi, vol. 2019, pages 1-12, April.
    5. Ali Hortaçsu & Jakub Kastl & Allen Zhang, 2018. "Bid Shading and Bidder Surplus in the US Treasury Auction System," American Economic Review, American Economic Association, vol. 108(1), pages 147-169, January.
    6. Ko, Bonggyun & Song, Jae Wook, 2018. "A simple analytics framework for evaluating mean escape time in different term structures with stochastic volatility," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 505(C), pages 398-412.

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